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Artificial Intelligence in Drug Discovery Analytical Framework

Artificial Intelligence has the potential to significantly improve drug discovery and development processes. The current page outlines the analytical framework developed to understand the comprehensive overview of components of the analytical framework.

Artificial Intelligence in Drug Discovery Analytical Framework

Advanced R&D

Biomarkers Development

Drug Discovery

Focus on Applications of AI for Drug Discovery 

The field of using Artificial Intelligence for drug discovery is a rapidly growing area of research that has the potential to revolutionize the process of drug discovery and development. 

AI-Assisted Diagnostics

At-Home Cancer Detection

Clinical Decision Support

Medical Images Analysis

Patients Outcome Prediction

Personalized Treatment Options 

Focus on Applications of AI for

Oncology Diagnostics and Treatment

There is a growing interest in the applications of AI for oncology diagnostics and treatment as the use of AI has the potential to greatly improve cancer care. AI algorithms can analyze large amounts of patient data, medical images, and treatment history to identify patterns and features that are associated with treatment response and toxicity, and use this information to develop personalized treatment plans for individual patients.

Compounds Classification



Identifying New Drug Candidates

Identifying New Drug Pathways

Identifying New Drug Structures

Identifying Drug to Drug Interactions

Identifying New Drug Indications

Identifying New Metabolic Pathways

Identifying Suitable Patients

Hit Identification

Lead Optimization

Predictive Drug Modeling

Target Identification


Imaging Analysis

Patient Stratification

Predictive Modeling

Real-Time Monitoring

Automated End-to-End 
Drug Analysis

Automated End-to-End Production

Predictive Patient Reaction Modeling

Virtual Experiment Processing

ADME/PK Modeling

Experiment Data Analyzing

Preclinical Protocol Optimization

Robotic Hands

 High Throughput Screening

Chemical Data  Analyzing

Clinical Trials Data Analyzing

Drug Safety Improving

Preclinical Trials Prediction

Preclinical Imaging Analysis





Data Analysis

Lab Experiments Data Analyzing

Established Drug Discovery-Oriented Entities

Early Drug Development

Early drug development is the stage of drug development that occurs before preclinical and clinical development. It involves identifying potential drug candidates, conducting initial testing to determine their pharmacological properties, and selecting candidates for further development. This stage has several peculiarities that distinguish it from other stages of drug development.

Clinical Drug Development

Clinical drug development is the stage of drug development that involves testing the safety and efficacy of a drug candidate in humans. This stage is typically divided into three phases, each with its peculiarities.

End-to-End Drug Development

End-to-end drug development is a comprehensive approach to drug development that involves all stages, from discovery to commercialization. The process can be divided into several stages, each of which has its peculiarities

Preclinical Development and Automation

AI has been increasingly used to support preclinical drug development by modeling the properties and potential outcomes of drug candidates. One way AI can do this is by analyzing the properties of a drug candidate's structure, such as its molecular weight, size, and shape, to predict its activity and efficacy. AI can also analyze genetic variations in specific cellular lines or mice strains to simulate preclinical studies and make predictions about potential efficacy and toxicity.

Data Processing

Data processing is an essential step in drug development as it involves analyzing and interpreting data to identify potential drug candidates and understand their safety and efficacy. 

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